Getting started with AI data visualization tools

When it comes to data, today's businesses may have too much of a good thing.

The world generates 402.74 million terabytes of data every day. Over 80% of organizations surveyed say they'll end up managing zettabytes worth of data. However, of those, only 36% say they can effectively manage it all.

Visualization is one of the best tools we have for turning data into actionable insights. It expresses data in a format that yields valuable insights even with a cursory glance. Data visualization tells a story about your business in an immediate, impactful format.

The problem? Creating good visualizations is usually the result of a long, expensive data exploration process. Not every company has the resources or time to invest in producing new dashboards and visualizations.

The good news is that AI data visualization tools are catching up to fill this gap. In this article, we'll look at how new AI-native BI tools make visual storytelling easier by automating the data analytics and visualization process for both technical and business users alike, with less time and money than traditional business intelligence.

The data deluge: Why traditional visualization falls short

The last two years produced 90% of the world's data. That's left both data teams and data stakeholders struggling to make sense of it all. Data teams are deluged with data requests and struggling to keep up. Meanwhile, data stakeholders need answers to burning questions now, before conditions change and they become irrelevant.

What causes this friction? In short, the nature of traditional BI.

Traditional BI delivers data from well-curated data pipelines. Prepping this data - aggregating, transforming, cleansing it - is an involved engineering process that can take days or weeks to complete.

The end result - a dashboard in a tool like Power BI - delivering a single, fixed view of this data. Users can't query these dashboards to find answers to new, unique questions that the author never considered. The fixed nature of the dashboard limits data stakeholders' imaginations and the type of questions they ask.

AI for BI turns this traditional process on its head. By combining natural language processing and the analytical capabilities of GenAI to combine, clean, and analyze data automatically, it enables new capabilities for both data stakeholders and data teams:

• Data stakeholders can do exploratory analysis to understand their dataset, and self-service answers to ad hoc questions, reducing the volume of support requests to data teams.
• Data teams can perform advanced and experimental analytics in a fraction of the time as traditional BI to reveal new insights.

What exactly are AI data visualization tools?

AI data visualization tools turn raw data into charts, graphs, and dashboards. Using generative AI (GenAI), they leverage Large Language Models (LLMs) to create the SQL and Python code required for the underlying data structures as well as the visualizations to present them.

AI-powered data visualization tools reduce the time required to produce new visualizations by automating the creation of data structures, queries, and coding. They can be used across a variety of use cases, including but not limited to:

• Understanding user behavior
• Digging into marketing campaign performance
• Performing advanced customer segmentation
• Building predictive analytics models

How do AI data visualization tools actually work?

A key feature of LLMs is their ability to model natural language and respond with their own generated output. In the case of AI data visualization, this means that LLMs can break down English language queries and convert them into programming languages, such as SQL and Python.

Using these natural language queries and the metadata for a given dataset, an AI data visualization tool can filter and aggregate the data it needs for an LLM to make an informed analysis. It can then pass this data to the LLM, which can summarize it, analyze it for interesting patterns, and produce informative graphs, charts, and other visualizations that best showcase the insights.

Behind the scenes, these tools leverage machine learning algorithms to continuously improve their understanding of data structures and user intent, making them smarter over time.

This breaks down a huge barrier for non-technical users who want faster access to their data. Instead of having to become adept at translating business requirements into SQL, they can state what they need directly in business language. The LLM handles translating this into code and delivering results.

Different types of AI data visualization tools

Not all AI data visualization tools take the same approach. There are at least three separate modalities, each with its own strengths and weaknesses.

AI-native platforms, like Fabi.ai and Julius, are built from the ground up to use AI. Instead of relying on traditional data workflows, they use LLMs natively  and thus don't suffer from the limitations of traditional BI tools. That means they can offer functionality that is inherently more flexible, scalable, and user-friendly.

By contrast, AI-augmented BI tools rely on traditional features, such as creating abstractions over SQL via useful but ultimately restrictive drag-and-drop interfaces. Tools such as Power BI Copilot and AI in Tableau from Microsoft and Tableau layer AI agents and assistants on top of existing data sources created and maintained by data engineering teams.

Finally, LLM-integrated notebooks take traditional data science notebooks, like Jupyter and Hex, add in AI features to generate code with natural-language queries. These tools aim to make life easier for developers with limited  support for a low-code or no-code experience for business users.

Why embrace AI for your data visualization needs?

AI-powered visualization brings a number of useful capabilities whether you're a business stakeholder, founder, PM or a data engineer, and if you have existing workflows or are starting from scratch.

Unlock deeper insights with speed

Standard workflows become easier and faster to implement with AI. LLM auto-generation of code can take the time required to create an initial running solution from hours, days, or weeks down to mere minutes.

Additionally, because AI data visualization tools aren't as constrained as traditional BI tools, you can unlock new insights you weren't able to access with a more traditional approach. AI can also spot patterns and trends in complex data that human users may have missed. For example, advanced visualizations like a hexbin analysis can identify instances of fraud that have previously gone undetected.

Empower non-technical users

Small companies can't necessarily afford to keep permanent BI engineers on board. Yet they still need to mine their data to determine how to improve the business.

Enabling AI data visualization means that any user, regardless of technical expertise, can mine their data for insights. This means, for example, product managers can perform in-depth research on the effectiveness of go-to-market campaigns or perform feature-level product analysis without investing in a data team.

Enable self-service

Even if you have data engineering resources on board, adding an AI data visualization tool gives all users a fast method of exploring their own data and getting the quick answers they need. This means that users don't have to send these questions to the data engineering team, which reduces their support workload and frees them up to focus on more strategic and technical tasks.

Many companies want to provide more self-service options to business users. Traditionally, building these out requires a significant up-front engineering investment: creating a data catalog, providing an intuitive UI for building visualizations and customizable dashboards, surfacing key business metrics, etc. AI data visualization tools offer many of the same benefits with greater flexibility and minimal up-front infrastructure investment. 

Key benefits of integrating AI data visualization tools

Accelerate data exploration and discovery

Users can leverage exploratory data analysis to quickly create advanced visualizations that provide insights into the underlying data. 

For example, a histogram can quickly show how values are distributed across various buckets, which can provide insights into, say, average customer order size. A correlation matrix can show which variables are most likely related, opening the door to a more complex multivariate analysis. 

Automate anomaly detection and pattern recognition

AI-powered data visualization can show deviations from patterns that you might never even realize existed. Many companies have used it, for example, to enable better fraud detection by identifying transactions that don’t fit a user’s historical behavior. Such transactions can be flagged for manual review, ultimately reducing the number of successful fraudulent transactions perpetrated against customer accounts. 

Create personalized and predictive visualizations

Traditional BI dashboards lock people into thinking one way about a problem. But data can be sliced and diced in a million different ways. 

For example, you can implement a predictive alerting system for user retention, issuing alerts via Slack to sales or customer reps in response to defined churn signals: 

• A user’s login frequency drops below 50% of the historical baseline
• A new user fails to complete critical setup sets
• A customer team is logging more or higher-severity support tickets than usual 

Improved decision-making and strategic planning

AI data visualization tools eliminate the gap between asking a question and receiving an answer. Companies and teams can leverage them to make data-driven decisions based on new and changing data within minutes, not weeks or months. 

Aisle, a marketing conversion platform, spent weeks working with its engineering teams to identify patterns in customer behavior that led to in-store purchases. Using AI data analysis and visualization, they reduced analysis time by 92%. That freed up 15 hours/week that the engineering team could use to focus on product and backend priorities instead of ad-hoc data requests.

Getting started: A step-by-step guide to implementing AI data visualization

Fortunately, implementing an AI data visualization tool requires minimal overhead. Most of the work will involve integrating it into the touchpoints of your existing data stack and IT architecture. Here are a few guidelines to get you started.

Step 1: Define your objectives and data sources

The first step is to identify the problem you want to solve and determine where you will get your data to tackle it. For most business users, this will involve uploading data in CSV, Excel, and Google Sheets, or connecting applications like Hubspot, Google Analytics, and others they use everyday. You can also include important internal data sources, such as a centralized data warehouse or data lake to get a comprehensive view of your business.

Step 2: Evaluate and select the right AI tool

An AI data visualization tool should, at a minimum, support:

• Pulling data from the data sources and formats you use
• Interactive charting and customizable charts to reflect your company’s branding and look-and-feel
• Collaborative workspaces, so that stakeholders and data engineers can work effectively together
• Round-tripping between AI-generated code and manually edited code, so that engineers and semi-technical stakeholders can spot check and  build on the results the LLM generates
• Snapshotting and reverting, so that you can revert to a known good state if you get off track

Step 3: Prepare and clean your data

A lot of traditional BI is focused on making data perfect for consumption. Fortunately, this isn't a do-or-die concern with AI data visualization tools, as you can generally use AI to get the data into the shape you need for analysis.

Your data should be of sufficient quality for an LLM to analyze and extract insights. Stakeholders who are deeply familiar with their data should be able to access the relevant data and tell, based on the results produced, whether it lines up with their knowledge of the business.

Step 4: Integrate and configure the solution

The integration involves connecting your data to the solution along with guardrails around sensitive data that can not be accessed by everyone in the company. AI data analytics solutions also have configurable settings for selecting data models, or giving context to specific data sources, and much more.

Step 5: Train your team and foster adoption

AI data visualization tools are, by definition, fairly intuitive to use. But that doesn't mean you can ignore training. Unless you evangelize the existence of this new tooling and its capabilities, many will fail to take proper advantage of it.

Hold one or more training sessions on your AI data visualization tool, including what problems can be solved with it and how users can expedite and automate the creation of data visualizations. Also, cover the basics of good data governance and privacy training to ensure that users are aware of company rules and industry regulations around the handling of sensitive data, such as personally identifiable information (PII).

Step 6: Monitor, iterate, and refine

Rather than unveil the new tool all at once, release it to a select group of data power users in both business and engineering functions. Gather feedback on what works and what doesn't, and track any measurable impact on your day-to-day data handling processes:

• Are ad hoc requests to the data engineering team decreasing?
• Do users feel they're getting value from the tool?
• What money-saving or revenue-producing opportunities have business users identified since adopting it?
• Are key business metrics and KPIs now accessible in real-time?

Adjust system configuration and your training program as needed, and then release to a wider group of users. Repeat this process iteratively.

Addressing common challenges in AI data visualization adoption

For the most part, AI data visualization "just works." But you may face some challenges in how the technology is adopted and used across the business.

Quality concerns

"Garbage in, garbage out" still applies in the AI world. If the data you supply to an AI data visualization tool is incorrect, the output will be incorrect as well. Additionally, LLMs can sometimes error and produce incorrect code.

At a minimum, all data output from AI solutions should be checked by business users to make sure it feels right and accords with their understanding of the business. For mission-critical reports, engineering personnel should validate the underlying generated code as well.

Data compliance issues

Users sharing data around in loose files can turn into a compliance nightmare. In particular, most companies need to keep careful track of customer data to comply with laws such as the General Data Protection Regulation (GDPR), as well as to ensure that PII remains secure.

User training plays a key role here. It also helps to select an AI data visualization tool that supports shared, collaborative workspaces, so that administrators can keep track of what data is being used where.

Addressing user resistance

You may face resistance from multiple avenues when implementing an AI data visualization solution. Business users might not understand how to use AI or when. Data engineers may fear the advent of "AI slop" and worry that they're being automated out of jobs.

It's important to pitch an AI data visualization tool, not as a worker replacement, but as a work assistant. For data engineers, for example, a data visualization tool can save time by generating the boring scaffolding code common to most projects, enabling them to spend more time on the 20% of the project that requires their technical expertise.

Getting started with an AI data visualization tool

Fabi.ai

Good AI data visualization starts with good tooling. We built Fabi.ai from the ground up as an AI-native tool for summarizing, analyzing, and visualizing data that caters to everyone in the company, no matter their level of technical expertise.

Fabi.ai generates SQL and Python code in a notebook format, enabling both no-code and low-code approaches to AI-powered data analysis. It enables automated data workflows for distribution via channels such as Slack and Google Sheets, creating interactive visualizations delivering actionable insights where the team operates.

Fabi also supports a number of advanced features, including self-service agents, automated workflows, snapshotting, and automatic versioning.

Other AI data visualization tools

Other tools take slightly different tacks on implementing an AI-native solution for AI data visualization:

• Julius is a user-friendly solution for non-technical users that enables quick visualization. However, it provides limited advanced customization options.
• Wisdom focuses on automated insight discovery, pattern recognition, and anomaly detection, but with less emphasis on creating your own unique, custom visualizations.
• isotopes.ai provides domain-specific AI analysis capabilities, tailoring its approach for specific industries. While useful, this usually requires more setup than general use cases.

Traditional BI tools are also increasingly supporting AI capabilities. While this can provide users with new and unique perspectives on their data, it's usually less flexible than AI-native solutions.

The future is visual: AI's evolving role in data storytelling

Data visualization tells us a story about our data. It shows, in a visceral manner, how trends occur and change over time, yielding insights that can drive meaningful change in the business.

Artificial intelligence frees data visualization from its prior constraints, enabling businesses to generate new insights and respond to ad hoc queries in minutes. A good AI data visualization tool provides a collaborative workspace where users can self-service answers to critical questions without the overhead of traditional BI.

To get started with AI data visualization, sign up for a free Fabi.ai account and start slicing and dicing your data immediately.